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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE76347"
# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE76347"
# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE76347.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE76347.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE76347.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/cohort_info.json"
# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each feature (row) in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# The dataset contains nasal epithelial cell gene expression data from microarray analysis
is_gene_available = True
# 2.1 Data Availability
# trait_row: Everyone has CF (key 0), so trait data is constant and not useful for association study
trait_row = None
# age_row: Age not recorded in characteristics
age_row = None
# gender_row: Gender not recorded in characteristics
gender_row = None
# 2.2 Data Type Conversion Functions (though not used since data unavailable)
def convert_trait(x):
if pd.isna(x):
return None
val = str(x).split(":")[-1].strip().upper()
if "CF" in val:
return 1
return None
def convert_age(x):
# Not used since age data not available
return None
def convert_gender(x):
# Not used since gender data not available
return None
# 3. Save Metadata
# is_trait_available is False since trait_row is None (constant trait value)
is_trait_available = False if trait_row is None else True
validate_and_save_cohort_info(is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available)
# 4. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)
# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])
print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# These are not human gene symbols but probe IDs from the Illumina array platform
# They need to be mapped to official human gene symbols for consistent analysis
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file)
# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# Extract probe ID and gene symbol mapping data
prob_col = 'ID' # Column containing probe identifiers matching genetic_df index
# Function to extract gene symbol from gene_assignment string
def extract_gene_symbol(text):
if pd.isna(text) or text == '---':
return None
# Split by // and take the second element which contains the gene symbol
parts = text.split('//')
if len(parts) >= 2:
return parts[1].strip()
return None
# Create mapping dataframe with proper gene symbols
mapping_df = gene_metadata[['ID']].copy()
mapping_df['Gene'] = gene_metadata['gene_assignment'].apply(extract_gene_symbol)
mapping_df = mapping_df.dropna()
# Apply mapping to convert probe data to gene data
gene_data = apply_gene_mapping(genetic_df, mapping_df)
# Preview the mapped gene expression data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:")
print(gene_data.index[:10])
print("\nPreview of expression values:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 2. Final validation - dataset not usable due to constant trait
empty_df = pd.DataFrame() # Empty dataframe since no linked data possible
is_biased = True # Explicitly mark as biased since trait is constant
validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False, # No usable trait data
is_biased=is_biased,
df=empty_df,
note="All subjects have CF (constant trait). Gene expression data saved but not suitable for trait association analysis."
) |